pydantic

Name: pydantic
Availability: InStock
Author: bswrundquist

Define Pydantic v2 data models with validation and serialization logic.

Updated Mar 9, 2026

Search Tags:#pydantic #python #validation #type hints #data modeling #serialization

Ready to run

npx skills add https://github.com/bswrundquist/devtools --skill pydantic-bswrundquist

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pydantic
Source: https://github.com/bswrundquist/devtools/tree/main/src/devtools/templates/claude/user/.claude/skills/pydantic
Command: npx skills add https://github.com/bswrundquist/devtools --skill pydantic-bswrundquist

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you efficiently define, validate, and serialize data using Pydantic v2, ensuring data integrity and simplifying complex data handling in your Python applications.

Core Features & Use Cases

Model Definition: Create clear, type-hinted data models.
Validation: Implement robust data validation with field_validator and model_validator.
Serialization: Control how your data is converted to and from various formats.
Use Case: When building an API, use Pydantic models to automatically validate incoming request data and serialize outgoing responses, ensuring consistency and catching errors early.

Quick Start

Use the pydantic skill to create a basic User model with an email field that must be a valid email address.

Frequently Asked Questions about pydantic

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I validate incoming API request data using Python type hints?▼

You can validate incoming API request data by defining Pydantic v2 models with Python type hints. The framework automatically validates incoming payloads against these models, catching type and constraint errors early before processing.

What is the best way to handle partial updates or PATCH requests with data models?▼

Handling PATCH requests with data models is supported through Pydantic v2 validation logic. You can use model validators and exclude_unset configurations to apply partial updates without overwriting existing fields with null values.

How do field_validator and model_validator work for custom data validation in Python?▼

Field_validator and model_validator work by allowing you to attach custom validation logic to specific fields or entire data models. Pydantic v2 executes these validators during instantiation to ensure complex data constraints are met.

Can I integrate Pydantic v2 data models with existing ORM frameworks?▼

Yes, you can integrate Pydantic v2 data models with existing ORM frameworks. The skill addresses common ORM integration patterns, allowing you to map database records directly to validated Pydantic models for consistent data handling.

How do discriminated unions and generic models handle dynamic data structures?▼

Discriminated unions and generic models handle dynamic data structures by allowing a single Pydantic model to represent multiple object shapes. This approach uses specific fields to route validation, ensuring complex nested data parses correctly.

Does pydantic-settings support environment variable management for application configuration?▼

Yes, pydantic-settings supports environment variable management for application configuration. It allows you to load settings from environment variables and validate them using standard Pydantic v2 data models.